• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.4
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• pp.128-134
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• 2022

Nakdong river estuary is being operated with the goal of expanding the period of seawater inflow from this year to 2022 every month and creating a brackish water area within 15 km of the upstream of the river bank. In this study, the deep learning algorithm Long Short-Term Memory (LSTM) was applied to predict the salinity of the Nakdong Bridge (about 5 km upstream of the river bank) for the purpose of rapid decision making for the target brackish water zone and prevention of salt water damage. Input data were constructed to reflect the temporal and spatial characteristics of the Nakdong River estuary, such as the amount of discharge from Changnyeong and Hamanbo, and an optimal model was constructed in consideration of the hydraulic characteristics of the Nakdong River Estuary by changing the degree according to the sequence length. For prediction accuracy, statistical analysis was performed using the coefficient of determination (R-squred) and RMSE (root mean square error). When the sequence length was 12, the R-squred 0.997 and RMSE 0.122 were the highest, and the prior prediction time showed a high degree of R-squred 0.93 or more until the 12-hour interval.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.157-166
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• 2022

Exercise is necessary for a healthy life, but it is recommended that it be conducted in a non-face-to-face environment in the context of an epidemic such as COVID-19. However, in the existing non-face-to-face exercise content, it is possible to recognize exercise movements, but the process of interpreting and providing feedback information is not automated. Therefore, in this paper, to solve this problem, we propose a method of creating a formalized rule to track the contents of exercise and the motions that constitute it. To make such a rule, first make a rule for the overall exercise content, and then create a tracking rule for the motions that make up the exercise. A motion tracking rule can be created by dividing the motion into steps and defining a key frame pose that divides the steps, and creating a transition rule between states and states represented by the key frame poses. The rules created in this way are premised on the use of posture and motion recognition technology using motion capture equipment, and are used for logical development for automation of application of these technologies. By using the rules proposed in this paper, not only recognizing the motions appearing in the exercise process, but also automating the interpretation of the entire motion process, making it possible to produce more advanced contents such as an artificial intelligence training system. Accordingly, the quality of feedback on the exercise process can be improved.

• Korean Association of Arts Management
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• no.59
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• pp.33-72
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• 2021

I intended to discuss the new phase of the publicness of museums in a digital environment with the Goole Arts & Culture Project. To this end, I critically examined the instrumental approaches and technological optimism in the application of digital technology to museums, and scrutinized the recent museological issues, particularly the revision or curtailment of the museum's publicness amid the spread of neoliberal policy, which have been omitted within those technological approaches. This is because the meaning of Google Art & Culture can be considered more effectively through an extended theoretical reconstruction. Based on these theoretical discussions, I critically reviewed how the "non-profit," an important concept that defines the publicness of museums, was adopted and utilized as an business strategy by Google. As a result, I wanted to reveal that the neoliberalization of museums, the failure of the government's public function, the crisis of museum's publicness, and Google's "non-profit" strategy have been closely related. Armed with advanced digital technology, the GAC project appropriated the publicness of museums as a useful profit-making model. As such, now the concept of publicness of museums is at a point of more controversial and radical transformation than ever before.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.141-147
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• 2022

Care farm recently has been introduced as social farm and green care centered on Europe, and has been developing rapidly in the last 20 years. Although it is being introduced gradually in Korea, it is still only at the beginning level, and it does not provide a guide for systematic farm owners' model and operation of the Care farm. In Care farm, advanced countries are making it possible to operate a more common care farm by providing a systematic national guide. By reviewing the literature of these guidelines in advanced countries, the contents are organized to help compose the guidelines for domestic care farm. In Canada, Ireland, the Netherlands, Norway and Finland, the contents of the guidelines for care farm by country can be broadly divided into general matters, how to start a care farm project for farm owners, understanding of target audience, farm organization system, farm management, and farm life. As the demand for care farms increases in Korea as well, guidelines for farm owners should be disseminated to facilitate access to care farms. It is also considered a good way to make a guide by combining it with the medical field, and it seems that it is necessary for the farmer to understand the medical field to some extent.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.649-662
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• 2022

As information security(IS) is recognized as an organization's core value, organizations are making efforts to adopt strict IS policies and technologies. However, strict IS policies can cause negative behavior for employees of organizations who need to apply IS to their work. This study confirms that IS can express the role stress of employees, and suggests a way to mitigate the IS role stress. Specifically, we confirm that organization justice and individual social identity can reduce IS role stress, which reduces IS voice behavior. In the study, we surveyed workers of organizations that applied IS policies to their work and obtained 318 samples. Also, we tested the hypothesis by applying the structural equation model. As a result, IS organization justice increased IS voice behavior through social identity and partially reduced IS voice behavior by mitigating IS role stress. In addition, social identity moderated the relationship between IS role stress and IS voice behavior. This study suggests strategies for achieving internal IS goals by suggesting conditions for mitigating IS role stress from an organizational and individual perspective.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.48-55
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• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.129-139
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• 2022

Analog-based streamers for ultra-high-resolution seismic surveys are capable of additional noise ingress in water, but the specifications cannot be expanded through interconnections. Foreign-produced digital streamers have been introduced and used primarily at domestic research institutes; however, the cost is high and smooth maintenance is challenging. This study investigates the localization of ultra-high-resolution digital streamers capable of high-resolution imaging of a geological structure. A digital streamer capable of 24-bit, 10 kHz digital sampling of up to 64 channel data was developed through research and development. Various quantitative specifications of the system were designed and developed close to the benchmark model, Geometrics' GeoEel streamer, and the number of modules that make up the system was drastically reduced, reducing development costs and making it easier to use. The field applicability of the developed streamer system was evaluated in an in situ experiment conducted in the waters around the Port of Yeong-il Bay in Pohang in April 2022.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.5
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• pp.54-68
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• 2022

This study proposes urban development concept and strategies for Garden City, focused on Solaseado, Yeongam Heanam Tourism-Leisure Type Enterprise City in Korea. Understanding that an essential element of a garden is the endless care performed by gardeners, the Garden City development concept suggests applying this idea to making planned cities by cultivating the potential natural landscape of the site in the long run. The meaning of Garden City can be defined in three aspects; an attitude and process of planning a city, a system for constructing the spatial structure of a city, and city branding. A Garden City is a city structured with the spirit of a garden, a city where open space networks become the urban structure, and a city that builds its identity through the landscape, respectively. From this point of view, the research draws development strategies with spatial design examples to embody the Garden City concept in Solaseado by following three steps; establishing the main urban axes, creating city networks through the conjunction of the axes, and categorizing and systematizing open spaces within the city. Consequently, the study shows an alternative urban planning model that extends the concept of a Garden City while maintaining the intrinsic landscape as an urban resource. In addition, the conceptual master plan of Solaseado will structure the urban landscape and park system according to the Garden City strategies.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.